Genetic Epidemiology最新文献

{"title":"Imputed Rare-Variant Aggregate Analysis Is a Powerful Approach to Detect Genetic Associations","authors":"Tianyi Liu,&nbsp;Paul L. Auer,&nbsp;Gao T. Wang,&nbsp;Elnaz Naderi,&nbsp;Andrew T. DeWan,&nbsp;Suzanne M. Leal","doi":"10.1002/gepi.70061","DOIUrl":"10.1002/gepi.70061","url":null,"abstract":"<div>\u0000 \u0000 <p>Imputation can cost-effectively generate genotypes for millions of variants. This study evaluated the power of performing rare-variant aggregate association tests using imputed genotypes. UK Biobank exome-sequence data and variants obtained via imputation using the HRC (r1.1) (<i>N</i> = 32,488 samples) or TOPMed (r3) (<i>N</i> = 133,597 samples) reference panels were analyzed. Simulations were used to compare the power of performing rare-variant aggregate association analysis using either sequence or imputed data. The number of genes available for analysis, that is, those with ≥ 2 rare variants, was approximately the same for exome-sequence and TOPMed imputed data, but HRC imputed data had ~10% fewer genes. For imputed data, using a less stringent R² threshold (i.e., &gt; 0.3 vs. &gt; 0.8) led to greater power to detect aggregate associations due to additional rare variants included in the analysis. Exome-sequence data provided the highest power to detect rare-variant aggregate associations. TOPMed imputed variants usually had &lt; 20% reduction in power in comparison to exome-sequence data, whereas HRC imputed variants provided substantially less power. Rare-variant aggregate association analyses using exome-sequence data, and imputed variants for <i>PCSK9</i> and low-density lipoprotein and <i>APOC3</i> and triglyceride levels were also performed. For these analyses, even when only ultra-rare variants (minor allele frequency &lt; 0.001) were analyzed, significant aggregate associations could be detected for all data types. Although there is a decrease in power for rare-variant aggregate association tests when analyzing imputed variants compared to sequence data due to missing variants and uncertainty in genotypes, analysis of imputed data is a viable approach to detect rare-variant aggregate associations when sequence data are unavailable.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149165781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A TWAS Method Calibrated for the Uncertainty of Predicted Expression","authors":"Arunabha Majumdar,&nbsp;Tanushree Haldar","doi":"10.1002/gepi.70060","DOIUrl":"10.1002/gepi.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>The transcriptome-wide association study (TWAS) is a powerful approach to identifying novel genes associated with complex phenotypes. Standard TWAS approaches build a prediction model for the genetic component of expression based on reference transcriptome data. Next, an outcome is regressed on the predicted expression in separate GWAS data. The traditional TWAS approach disregards the uncertainty of predicted expression, which can lead to unreliable inference on gene-phenotype associations. We propose a novel approach that adjusts for the uncertainty of predicted expression in TWAS. We adapt techniques from measurement error theory and implement bootstrapping algorithms for penalized regression to explicitly obtain an adjustment factor to be incorporated into the unadjusted TWAS. We base the framework on adaptive Lasso. Using extensive simulations, we show that our approach produces more accurate estimates of the gene's effect size than a traditional TWAS approach. Traditional TWAS marginally inflates the type I error rate, whereas the adjusted TWAS adequately controls it. At the expense of a modestly inflated false positive rate, an unadjusted TWAS offers a limited increase in power compared to the adjusted TWAS. Simulations show that some other unadjusted and unified TWAS approaches can also inflate the type I error rate, particularly when using summary-level data. We demonstrate the merits of the proposed adjusted approach by conducting TWAS for height and lipid phenotypes, LDL and HDL cholesterol, and triglycerides while integrating the Geuvadis transcriptome and UK Biobank GWAS data.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149136262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refining Genetic Instruments for Dietary Intake Mendelian Randomization Using Phenome-Wide Association Studies","authors":"Kristen J. Sutton,&nbsp;Julie E. Gervis,&nbsp;Moomal Jatoi,&nbsp;Liang-Dar Hwang,&nbsp;Audrey E. Hendricks,&nbsp;Debashis Ghosh,&nbsp;Kenneth E. Westerman,&nbsp;Joanne B. Cole","doi":"10.1002/gepi.70059","DOIUrl":"10.1002/gepi.70059","url":null,"abstract":"<p>Most Mendelian randomization (MR) of dietary intake use the full set of genome-wide significant (GWS) variants in the instrumental variable (IV), likely biasing causal estimates due to pleiotropy. To characterize the common methods to handle pleiotropy in dietary intake MR, we conducted a scoping review of the literature on dietary intake MR studies. We extracted information on IV construction, assessment of pleiotropy, and sensitivity analyzes revealing that only 20% of studies used an IV with functional plausibility. In the absence of functionally-informed IVs, we tested if two-sample MR using GWS variants filtered for pleiotropic associations through phenome-wide association studies (PheWAS) could identify diet–health relationships supported by existing nutrition science, focusing on oily fish and alcohol intake, the latter of which has a functionally-informed IV for comparison (rs1229984 in the <i>ADH1B</i> gene). To further explore this question, we performed multivariable MR and employed MR-CAUSE. The numerous models consistently supported that oily fish reduced triglycerides. In contrast, GWS and PheWAS-filtered IVs suggested that alcohol decreased alanine aminotransferase levels, whereas the functional IV (rs1229984) found the opposite expected relationship. Isolating the direct effect of dietary intake from GWS IV remains challenging. Future work should focus on identifying functional variants impacting dietary behavior.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13628697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genetic Determinants of Vascular Dementia: Blood Metabolite Associations and Candidate Therapeutic Target Prioritization","authors":"Chengwei Chen,&nbsp;Xiang Zou,&nbsp;Jie Hu,&nbsp;Chuanzhi Duan,&nbsp;Xifeng Li,&nbsp;Hongbo Guo","doi":"10.1002/gepi.70056","DOIUrl":"https://doi.org/10.1002/gepi.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>Vascular dementia (VaD), caused by reduced cerebral blood flow, is a common form of dementia lacking effective treatments. This study used genome-wide association study (GWAS) summary data, Mendelian randomization (MR), and colocalization analyzes to prioritize blood metabolites and candidate therapeutic targets associated with VaD. Key analyzes included metabolic pathway enrichment, a descriptive Phenome-Wide Association Study (PheWAS) of the broader curated association profiles of prioritized genes, mediation analysis, and in-silico compound prioritization with molecular docking. Twenty-four metabolites were associated with VaD, with taurocholate and glutaroyl carnitine prioritized as tier 1 metabolite–gene pairs. Pathway analysis implicated aminoacyl-tRNA and branched-chain amino acid biosynthesis pathways. Apolipoprotein E (APOE) and glutaryl-CoA dehydrogenase (GCDH) were prioritized candidate genes. The PheWAS recapitulated APOE's curated associations with Alzheimer's disease and lipid traits, whereas GCDH had a more restricted curated association profile dominated by glutaric acidemia type I; these patterns provide pleiotropic context but do not establish safety. Single-cell RNA-sequencing analysis localized APOE predominantly to microglia and astrocytes and GCDH predominantly to oligodendrocytes, providing cell-type-specific context. Mediation analysis identified a statistically supported indirect pathway involving alpha-2-macroglobulin receptor-associated protein for taurocholate. Aflatoxin B1 was highly ranked in an in-silico query but, because of its established toxicity, is not a therapeutic candidate. This study prioritizes metabolic and genetic candidates for experimental and clinical follow-up rather than establishing therapeutic targets.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 8","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149072512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 2026 Annual Meeting of the International Genetic Epidemiology Society","authors":"","doi":"10.1002/gepi.70057","DOIUrl":"https://doi.org/10.1002/gepi.70057","url":null,"abstract":"","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing the Shared Genetic Architecture of Metabolic Dysfunction–Associated Steatotic Liver Disease–Related Traits Through Genomic Structural Equation Modeling","authors":"Hao Dong,&nbsp;Yufeng Li,&nbsp;Qiang Ding,&nbsp;Miaoxin Zhang,&nbsp;Shuaipeng Gu,&nbsp;Mei Liu","doi":"10.1002/gepi.70055","DOIUrl":"10.1002/gepi.70055","url":null,"abstract":"<div>\u0000 \u0000 <p>Although individual traits related to metabolic dysfunction–associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F–associated genes, including <i>ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1</i>, and <i>ABHD17C</i>. We observed significant enrichment of MASLD-F–related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.</p></div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148897198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mendelianization: Concentrating Polygenic Signal Into a Single Causal Locus","authors":"Eric V. Strobl","doi":"10.1002/gepi.70053","DOIUrl":"https://doi.org/10.1002/gepi.70053","url":null,"abstract":"<p>Complex disorders such as depression and alcohol use involve numerous genetic variants, and implicated loci continue to grow with sample size. This proliferation hampers interpretability, as the mechanisms by which so many variants jointly contribute to pathophysiology remain unclear. In contrast, classical Mendelian diseases arise from a single causal locus and are easier to interpret. We thus introduce <i>Mendelianization</i>—an algorithm distinct from Mendelian randomization—that learns weighted combinations of outcomes so that each aggregated phenotype concentrates association at one locus. We prove that this locus is causal under four structural assumptions natural to genetic data. The method handles partial sample overlap, provides calibrated hypothesis tests, maps coefficients to interpretable scales, and quantifies the degree of Mendelianism using summary <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 \u0000 <mrow>\u0000 <mi>z</mi>\u0000 </mrow>\u0000 </mrow>\u0000 <annotation> $z$</annotation>\u0000 </semantics></math>-statistics alone. In experiments, Mendelianization enhances statistical power to detect Mendelian symptom profiles even in heterogeneous disorders like major depression, generalized anxiety, and alcohol use disorder. An R implementation is available at github.com/ericstrobl/Mendelianization.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148754044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Inference With Ghostknockoffs in Genome-Wide Association Studies With Sample Relatedness","authors":"Xinran Qi,&nbsp;Michael E. Belloy,&nbsp;Jiaqi Gu,&nbsp;Xiaoxia Liu,&nbsp;Hua Tang,&nbsp;Zihuai He","doi":"10.1002/gepi.70051","DOIUrl":"10.1002/gepi.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex traits. Recent studies show that knockoff-based methods can identify variants with unique, potentially causal effects on phenotypes. However, their statistical validity and effectiveness in studies with related individuals, such as the UK Biobank, remain unexplored. In this paper, we extensively evaluate a simple and effective analytical strategy that integrates GhostKnockoffs and state-of-the-art marginal association tests. We show that this approach is robust to arbitrary relatedness structure as long as the input <i>Z</i>-scores are derived from valid generalized linear mixed models. This robustness also extends GhostKnockoffs to other GWASs settings, including meta-analysis of studies with sample overlap when the input score test <i>Z</i>-scores are properly calibrated, and association test statistics beyond score tests in independent sample settings. We demonstrate the method's validity and practical utility using simulation studies and a meta-analysis of nine European ancestral genome-wide association studies and whole exome/genome sequencing studies for the Alzheimer's disease.</p>\u0000 </div>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Omnicausal Model Reveals the Highly Polyfactorial Nature of Complex Diseases","authors":"Carla Márquez-Luna,&nbsp;Martin Tournaire,&nbsp;Ghislain Rocheleau,&nbsp;Ron Do,&nbsp;Marie Verbanck","doi":"10.1002/gepi.70052","DOIUrl":"https://doi.org/10.1002/gepi.70052","url":null,"abstract":"<p>Mendelian randomization (MR) is a human genetics method for inferring causal relationships between risk factors and diseases. A common focus of MR studies has been on the causal inference of a single risk factor on a single disease. This has led to the successful discovery of numerous causal risk factors for disease. However, it remains unclear how much each causal risk factor contributes to disease collectively. Here, we introduce the concept of “causality explained,” that provides an estimator of the causal variance explained by a phenome-wide set of risk factors on complex diseases to assess how much causality can be potentially explained. The model is based on principal component regression which is a multivariate linear regression based on principal component analysis. In complement, we propose the “polyfactorial index” to assess the trajectory of causality explained as risk factors are sequentially added into the model, to characterize the causal architecture for a complex disease. We demonstrate that our model correctly assesses the causality explained and causal architecture in simulations across a wide range of parameters. To build our model, we used a phenome-wide set of 222 traits from the UK Biobank compared to a set of 5 known risk factors for coronary artery disease. We observed that the phenome-wide set explains more than 45% of causality compared to 28.76% for the set of known risk factors. In addition, we tested our approach on 13 complex diseases and showed that the phenome-wide set can explain between 27% for anorexia to 80% for schizophrenia, with ever increasing trajectories of causality explained. We propose the “omnicausal model,” which posits that a large number of risk factors individually explains a small amount to disease but collectively explain most of the causal variance. We distinguished core and peripheral causal factors that explain respectively a larger and a smaller part of causal variance. This approach provides insights into the underlying causal architecture between risk factors and disease.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying Bayesian Multivariable Mendelian Randomisation to Prioritise Candidate Causal Traits From High-Dimensional Data: Illustration From Estimation of the Effect of Maternal Metabolites on Offspring Birthweight","authors":"Ciarrah-Jane Barry,&nbsp;Verena Zuber,&nbsp;Deborah A. Lawlor,&nbsp;Maria Carolina Borges,&nbsp;Eleanor Sanderson,&nbsp;Chin Yang Shapland","doi":"10.1002/gepi.70043","DOIUrl":"10.1002/gepi.70043","url":null,"abstract":"<p>Mendelian randomisation (MR) is an approach to causal inference that uses genetic variants to infer whether or not a causal effect exists, unbiased by unobserved confounding. MR estimation usually considers the effect of a single exposure on an outcome; it has recently been extended to explore potential effects of multiple exposures using multivariable MR (MVMR). Existing MVMR models are restricted to a few exposure traits in a single estimation, particularly if those traits are highly correlated. However, for many relationships of interest there are many highly correlated exposures which may have a causal effect on the outcome. MVMR Bayesian model averaging (MVMR-BMA) provides a hypothesis-free exposure selection approach with many correlated exposures. Although potentially very powerful, BMA approaches to estimation are not commonly applied in epidemiological studies. Here we describe the application of MVMR-BMA to the selection of maternal metabolites that are causal for offspring birthweight. We describe the inputs and outputs of the model in detail and discuss the appropriate sensitivity analyses, illustrating these with our application. Through this, we hope to provide a guide to help other researchers, who are potentially unfamiliar with the terminology of Bayesian analysis, but would like to apply the method to their data.</p>","PeriodicalId":12710,"journal":{"name":"Genetic Epidemiology","volume":"50 6","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gepi.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148351682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}